Bi-directional and shared epigenomic signatures following proton and 56Fe irradiation.

The brain's response to radiation exposure is an important concern for patients undergoing cancer therapy and astronauts on long missions in deep space. We assessed whether this response is specific and prolonged and is linked to epigenetic mechanisms. We focused on the response of the hippocampus at early (2-weeks) and late (20-week) time points following whole body proton irradiation. We examined two forms of DNA methylation, cytosine methylation (5mC) and hydroxymethylation (5hmC). Impairments in object recognition, spatial memory retention, and network stability following proton irradiation were observed at the two-week time point and correlated with altered gene expression and 5hmC profiles that mapped to specific gene ontology pathways. Significant overlap was observed between DNA methylation changes at the 2 and 20-week time points demonstrating specificity and retention of changes in response to radiation. Moreover, a novel class of DNA methylation change was observed following an environmental challenge (i.e. space irradiation), characterized by both increased and decreased 5hmC levels along the entire gene body. These changes were mapped to genes encoding neuronal functions including postsynaptic gene ontology categories. Thus, the brain's response to proton irradiation is both specific and prolonged and involves novel remodeling of non-random regions of the epigenome

Regularizing Deep Networks by Modeling and Predicting Label Structure

We construct custom regularization functions for use in supervised training of deep neural networks. Our technique is applicable when the ground-truth labels themselves exhibit internal structure; we derive a regularizer by learning an autoencoder over the set of annotations. Training thereby becomes a two-phase procedure. The first phase models labels with an autoencoder. The second phase trains the actual network of interest by attaching an auxiliary branch that must predict output via a hidden layer of the autoencoder. After training, we discard this auxiliary branch. We experiment in the context of semantic segmentation, demonstrating this regularization strategy leads to consistent accuracy boosts over baselines, both when training from scratch, or in combination with ImageNet pretraining. Gains are also consistent over different choices of convolutional network architecture. As our regularizer is discarded after training, our method has zero cost at test time; the performance improvements are essentially free. We are simply able to learn better network weights by building an abstract model of the label space, and then training the network to understand this abstraction alongside the original task.Comment: to appear at CVPR 201

A Survey on Visual Mamba

State space models (SSM) with selection mechanisms and hardware-aware architectures, namely Mamba, have recently shown significant potential in long-sequence modeling. Since the complexity of transformers’ self-attention mechanism is quadratic with image size, as well as increasing computational demands, researchers are currently exploring how to adapt Mamba for computer vision tasks. This paper is the first comprehensive survey that aims to provide an in-depth analysis of Mamba models within the domain of computer vision. It begins by exploring the foundational concepts contributing to Mamba’s success, including the SSM framework, selection mechanisms, and hardware-aware design. Then, we review these vision Mamba models by categorizing them into foundational models and those enhanced with techniques including convolution, recurrence, and attention to improve their sophistication. Furthermore, we investigate the widespread applications of Mamba in vision tasks, which include their use as a backbone in various levels of vision processing. This encompasses general visual tasks, medical visual tasks (e.g., 2D/3D segmentation, classification, image registration, etc.), and remote sensing visual tasks. In particular, we introduce general visual tasks from two levels: high/mid-level vision (e.g., object detection, segmentation, video classification, etc.) and low-level vision (e.g., image super-resolution, image restoration, visual generation, etc.). We hope this endeavor will spark additional interest within the community to address current challenges and further apply Mamba models in computer vision

On Neuromechanical Approaches for the Study of Biological Grasp and Manipulation

Biological and robotic grasp and manipulation are undeniably similar at the level of mechanical task performance. However, their underlying fundamental biological vs. engineering mechanisms are, by definition, dramatically different and can even be antithetical. Even our approach to each is diametrically opposite: inductive science for the study of biological systems vs. engineering synthesis for the design and construction of robotic systems. The past 20 years have seen several conceptual advances in both fields and the quest to unify them. Chief among them is the reluctant recognition that their underlying fundamental mechanisms may actually share limited common ground, while exhibiting many fundamental differences. This recognition is particularly liberating because it allows us to resolve and move beyond multiple paradoxes and contradictions that arose from the initial reasonable assumption of a large common ground. Here, we begin by introducing the perspective of neuromechanics, which emphasizes that real-world behavior emerges from the intimate interactions among the physical structure of the system, the mechanical requirements of a task, the feasible neural control actions to produce it, and the ability of the neuromuscular system to adapt through interactions with the environment. This allows us to articulate a succinct overview of a few salient conceptual paradoxes and contradictions regarding under-determined vs. over-determined mechanics, under- vs. over-actuated control, prescribed vs. emergent function, learning vs. implementation vs. adaptation, prescriptive vs. descriptive synergies, and optimal vs. habitual performance. We conclude by presenting open questions and suggesting directions for future research. We hope this frank assessment of the state-of-the-art will encourage and guide these communities to continue to interact and make progress in these important areas

Whole-genome analysis of histone H3 lysine 27 trimethylation in Arabidopsis

Trimethylation of histone H3 lysine 27 (H3K27me3) plays critical roles in regulating animal development, and in several cases, H3K27me3 is also required for the proper expression of developmentally important genes in plants. However, the extent to which H3K27me3 regulates plant genes on a genome-wide scale remains unknown. In addition, it is not clear whether the establishment and spreading of H3K27me3 occur through the same mechanisms in plants and animals. We identified regions containing H3K27me3 in the genome of the flowering plant Arabidopsis thaliana using a high-density whole-genome tiling microarray. The results suggest that H3K27me3 is a major silencing mechanism in plants that regulates an unexpectedly large number of genes in Arabidopsis (~4,400), and that the maintenance of H3K27me3 is largely independent of other epigenetic pathways, such as DNA methylation or RNA interference. Unlike in animals, where H3K27m3 occupies large genomic regions, in Arabidopsis, we found that H3K27m3 domains were largely restricted to the transcribed regions of single genes. Furthermore, unlike in animals systems, H3K27m3 domains were not preferentially associated with low-nucleosome density regions. The results suggest that different mechanisms may underlie the establishment and spreading of H3K27me3 in plants and animals

SALSA: A Novel Dataset for Multimodal Group Behavior Analysis

Studying free-standing conversational groups (FCGs) in unstructured social settings (e.g., cocktail party ) is gratifying due to the wealth of information available at the group (mining social networks) and individual (recognizing native behavioral and personality traits) levels. However, analyzing social scenes involving FCGs is also highly challenging due to the difficulty in extracting behavioral cues such as target locations, their speaking activity and head/body pose due to crowdedness and presence of extreme occlusions. To this end, we propose SALSA, a novel dataset facilitating multimodal and Synergetic sociAL Scene Analysis, and make two main contributions to research on automated social interaction analysis: (1) SALSA records social interactions among 18 participants in a natural, indoor environment for over 60 minutes, under the poster presentation and cocktail party contexts presenting difficulties in the form of low-resolution images, lighting variations, numerous occlusions, reverberations and interfering sound sources; (2) To alleviate these problems we facilitate multimodal analysis by recording the social interplay using four static surveillance cameras and sociometric badges worn by each participant, comprising the microphone, accelerometer, bluetooth and infrared sensors. In addition to raw data, we also provide annotations concerning individuals' personality as well as their position, head, body orientation and F-formation information over the entire event duration. Through extensive experiments with state-of-the-art approaches, we show (a) the limitations of current methods and (b) how the recorded multiple cues synergetically aid automatic analysis of social interactions. SALSA is available at http://tev.fbk.eu/salsa.Comment: 14 pages, 11 figure

G protein-coupled receptor heteromers are key players in substance use disorder

International audienceG protein-coupled receptors (GPCR) represent the largest family of membrane proteins in the human genome. Physical association between two different GPCRs is linked to functional interactions which generates a novel entity, called heteromer, with specific ligand binding and signaling properties. Heteromerization is increasingly recognized to take place in the mesocorticolimbic pathway and to contribute to various aspects related to substance use disorder. This review focuses on heteromers identified in brain areas relevant to drug addiction. We report changes at the molecular and cellular levels that establish specific functional impact and highlight behavioral outcome in preclinical models. Finally, we briefly discuss selective targeting of native heteromers as an innovative therapeutic option